Triple
T3749715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mudgee |
E81296
|
entity |
| Predicate | nearbyTown |
P3883
|
FINISHED |
| Object | Gulgong |
E379133
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Gulgong | Statement: [Mudgee, nearbyTown, Gulgong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gulgong Context triple: [Mudgee, nearbyTown, Gulgong]
-
A.
Gulgong
chosen
Gulgong is a historic gold rush town in New South Wales, Australia, known for its well-preserved 19th-century streetscapes and heritage buildings.
-
B.
Tangtse
Tangtse is a village in the Leh district of Ladakh, India, situated along key routes between the Indus Valley and the Pangong Tso region in the Himalayas.
-
C.
Gubongsan
Gubongsan is a mountain located in or near the city of Daejeon in South Korea, known for its hiking trails and scenic views.
-
D.
Guting
Guting is a key Taipei Metro station in central Taipei that serves as a transfer point between multiple subway lines.
-
E.
Gwangalli
Gwangalli is a coastal neighborhood in Busan, South Korea, best known for its sandy beach, vibrant nightlife, and scenic views of the nearby Gwangan Bridge.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad8b19b7b08190a6188804e99c53e9 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb6d0ac4819092c9a41cc60f518d |
completed | March 8, 2026, 7:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4db31f964819087bab143f638754f |
completed | March 14, 2026, 3:51 a.m. |
Created at: March 8, 2026, 3:35 p.m.